A Boosted Particle Swarm Method for Energy Efficiency Optimization of PRO Systems

Yingxue Chen and Linfeng Gou
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Yingxue Chen: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China
Linfeng Gou: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China

Energies, 2021, vol. 14, issue 22, 1-13

Abstract: The analytical solutions of complex dynamic PRO systems pose challenges to ensuring that maximum power can be harvested in stable, rapid, and efficient ways in response to varying operational environments. In this paper, a boosted particle swarm optimization (BPSO) method with enhanced essential coefficients is proposed to enhance the exploration and exploitation stages in the optimization process. Moreover, several state-of-the-art techniques are utilized to evaluate the proposed BPSO of scaled-up PRO systems. The competitive results revealed that the proposed method improves power density by up to 88.9% in comparison with other algorithms, proving its ability to provide superior performance with complex and computationally intensive derivative problems. The analysis and comparison of the popular and recent metaheuristic methods in this study could provide a reference for the targeted selection method for different applications.

Keywords: pressure retarded osmosis (PRO); metaheuristic algorithms; boosted particle swarm optimization; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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